Ocean Salinity Science - Exeter, UK November Sea Surface Salinity from space: a promising future for operational oceanography? B. Tranchant, E. Greiner, G. Garric, M. Drevillon and C. Regnier

Ocean Salinity Science - Exeter, UK November Goal of SSS from space in operational oceanography? To understand the impact of new SSS data on estimates of surface freshwater fluxes (E-P)  difficult to estimate.  Mixed layer depth  Barrier layer  Heat fluxes  Consistency with other ocean observations To understand the complementarity of ARGO and Aquarius/SMOS data in data assimilation.  Consistency with other ocean observations (e.g. OSEs and OSSEs) To provide improved information about a time-varying near surface salinity field.

Ocean Salinity Science - Exeter, UK November Theory: OSSE in Atlantic (1/3°) performed in 2007 see: Tranchant et al., 2008, Remote Sensing of Environment and Tranchant et al., 2008, Operational Oceanography Variance (PSU 2 ) 1.The impact of the Aquarius L2 Products was weak compared to the SMOS L2 Products  space and time coverage 2.The assimilation of SMOS L2 was a better approach than the assimilation of SMOS L3 with a model at 1/3°. Time (year 2003) SMOSAQUA SSS Observation Error No large scale error, no bias and no E-P flux correction in the Data Assimilation system !

Ocean Salinity Science - Exeter, UK November SSS in operational oceanography Hydrological cycle errors and SSS Rainfalls fluxes errors and SSS spatial errors structures model (ERAI rainfall flux ) – model (GPCPV2.1) SSS model (ERAI rainfall flux ) – SSS climatology (levitus 98) Fresher SSS anomaly in the tropics and saltier anomaly at mid-latitudes SSS anomallies : Similar patterns  Particularly in the tropical band. SSS Anomaly (2002)

Ocean Salinity Science - Exeter, UK November ARGO vs Aquarius and SMOS in the global operational ocean forecasting system at 1/12° Analysis – in-situ : residual 2013 Analysis – Aquarius (V3.0) Analysis – SMOS (LOCEAN)

Ocean Salinity Science - Exeter, UK November Dominant mode of SSS variability over the period : EOFs at mid-latitudes (-40°S-40°N)  Modes are quite equivalent in the equatorial regions but inversed #1 #2 #1 #2 #3 SMOS (L3/AD, 10 days from LOCEAN)Aquarius (L3/7 days V2.0)

Ocean Salinity Science - Exeter, UK November –Global ocean forecasting system at ¼° and 50 vertical levels –Period  September 2011-April 2012 (With and Without D.A. of various L3 SSS Aquarius data products, CAP, V1.3 weekly and V2.0 daily and weekly) –Observation Error : Regression error with the Aquarius error (ARGO – Aquarius) function of SST and the SST 2 and some distance to the coast (RFI + mesoscale pattern). (best fit) Practice: OSE with the Global Ocean forecasting system at ¼° of Mercator ocean performed in 2012

Ocean Salinity Science - Exeter, UK November SSS Bias with DA of Aquarius V2.0 Valuable informations from AQUARIUS data are still dominated by large scale biases. This biases vary with time, with a prominent seasonal signal. Innovation (insitu – model)

Ocean Salinity Science - Exeter, UK November Results with 7 days V2.0 data: impact on in-situ (global) Bias: mean misfit (obs. - model forecast) Without DA of SSSWith DA of SSS Salinity profiles Temperature profiles Strengthening of a positive bias near the sea surface  freshening trend Lower impact in sub- surface (model is saltier than observations) No important changes Slight positive bias  Model forecast is : colder than observations (0-800 m) Warmer than observations (beyond 2000 m)

Ocean Salinity Science - Exeter, UK November Results: impact on in-situ (South Indian) mean and rms difference between obs. and model forecast (Salinity) Strengthening of a positive bias near the sea surface  freshening trend RMS difference is not significantly impacted Without DA of SSS With DA of SSS

Ocean Salinity Science - Exeter, UK November Results: Score (Global & North tropical pacific) mean and rms difference between obs. and model forecast (Salinity) 0.5 PSU : AQUARIUS 0.2 PSU : Insitu 0.2 PSU : AQUARIUS 0.1 PSU : Insitu GLOBAL N. Tropical Pacific

Ocean Salinity Science - Exeter, UK November Without DA of SSSWith DA of SSS Impact on SSS where few in-situ data are available Mean and RMS difference between obs. and model forecast Bias and error improvement for SSS Aquarius and in-situ RMS improvement : PSU Bias improvement

Ocean Salinity Science - Exeter, UK November Operational System in Indonesia (1/12° including tides) – INDESO Validation : monthly SSS data vs model ( ) Aquarius V3.0SMOS (LOCEAN)JAMSTEC (ARGO,TRITON, CTD) model data model - data R=0.851 Nobs=46343 R=0.553 Nobs=71342 R=0.868 Nobs=36693 mean=0.025 rms=0.49 mean=0.024 rms=0.66 mean=0.015 rms=0.49

Ocean Salinity Science - Exeter, UK November Aquarius V3.0SMOS (LOCEAN) JAMSTEC Bias RMSD Operational System in Indonesia (1/12° including tides) – INDESO validation : monthly SSS data vs model ( )

Ocean Salinity Science - Exeter, UK November SSS biais in South China Sea: in-situ validation 2 weeks August weeks December 2012 Biais is in the model !

Ocean Salinity Science - Exeter, UK November Conclusions Important biases exist in SSS measured from space –May introduce biases in some regions: Equatorial band (ITCZ, SPCZ) etc –Aquarius/SMOS data look similar to altimetry with a large orbit error? Biases still exist in operational model –With and without DA –Rainfall fluxes errors Data assimilation of Aquarius data V2.0: –Has a sligthly positive impact on the system. –Does not disrupt equilibrium with other data : unchanged assimilation diagnostics (RMS of SST, SSS, SLA innovation at global scale) –Has the ability to detect meso-scale features even in mid-latitudes and in cloudy conditions, but this potential is still limited by the large scale biases. –Can fill in-situ data gap ( Arabian sea, Bay of Benguale, Amazon, Indonesia SCS, etc..)

Ocean Salinity Science - Exeter, UK November Perspectives Dedicated impact studies with the new SMOS and Aquarius data and improved data assimilation schemes are required to better understand the SSS (hydrological cycle) –Remove the bias before assimilating SSS is an important issue  Biais correction of SSS (3Dvar) –Adaptive tuning of observations errors to fit with others errors (model and observations) Estimate observation error covariance matrix R using innovation statistics (Desrozier et al., 2005): –Assimilate other SSS data : L2/L3/L4 ?, SMOS and Aquarius data together –Work with Data Production Center to better understand/assimilate data we use  best strategy? OSSEs to define future requirements of salinity missions by taking into account: –Argo measurements –Last versions of DA systems More fundamental work on SSS data assimilation are required –Correction of freshwater fluxes, –Assimilation of brightness temperatures –4D error covariances, ensemble approach

Ocean Salinity Science - Exeter, UK November Adaptive tuning of observations errors Ideally, ratio=1 ratio obs. error overestimated ratio > 1 => obs. error underestimated Ratio Desroziers = [ residual (innovation) T ] R E Jason1 SST Envisat The observation errors in the assimilation systems is often a rough estimate… The objective of this diagnostic is to improve the error specification by tuning an adaptive weight coefficient  acting on the error of each assimilated observation. 

Ocean Salinity Science - Exeter, UK November Adaptive tuning of observations errors Ideally, ratio=1 ratio obs. error overestimated ratio > 1 => obs. error underestimated Ratio Desroziers = [ residual (innovation) T ] R E Jason1 SST Envisat The prescription of observation errors in the assimilation systems is often too approximate...

Ocean Salinity Science - Exeter, UK November Adaptive tuning of observations errors - SLA - cm Envisat error on without tuning cm Envisat error on with tuning Fit Slope= 0.78Fit Slope= 0.71

Ocean Salinity Science - Exeter, UK November Mode of variability vs innovation of SSS Mean SSS innovation (2013) Xie, P., T. Boyer, E. Bayler, Y. Xue, D.Byrne, J. Reagan, R. Locarnini, F. Sun,R. Joyce, and A. Kumar (2014), An in situ-satellite blended analysis of global sea surface salinity, J. Geophys. Res.Oceans, 119, 6140–6160, doi: /2014JC

Ocean Salinity Science - Exeter, UK November –Global ocean forecasting system at ¼° and 50 vertical levels Ocean Model : ORCA025 LIM2 EVP from NEMO3.1 3 hourly atmospheric forcing from ECMWF (Bulk Formulae from CORE) Data Assimilation system : SAM2v1 (SEEK kernel: Reduced Order Kalman Filter) –FGAT (First Guess at Appropriate Time) –IAU : Incremental Analysis Update –Bias correction from 3Dvar (in-situ) Assimilated data –SST from AMSRE-AVHRR at ¼° –SLA from Jason1, Jason 2, ENVISAT –In-situ profiles from CORIOLIS centre –Period  September 2011-April 2012 (With and Without D.A. of various L3 SSS Aquarius data products, CAP, V1.3 weekly and V2.0 daily and weekly) –Observation Error : Regression error with the Aquarius error (ARGO – Aquarius) function of SST and the SST 2 and some distance to the coast (RFI + mesoscale pattern). (best fit) Practice: OSE with the Global Ocean forecasting system at ¼° of Mercator ocean performed in 2012

Ocean Salinity Science - Exeter, UK November –Need to have an appropriate Observation operator innovation = obs. - model equivalent Model equivalent : where [] denotes a weekly mean and denotes a spatial mean (shapiro filter ~ 1°) –Observation Error : comes from a regression error with the Aquarius error (ARGO – Aquarius) function of SST and the SST 2 and some distance to the coast (RFI + mesoscale pattern). (best fit) First OSE with Aquarius data Exemple of observation error on October 7, 2011

Ocean Salinity Science - Exeter, UK November SSS errors in operational oceanography Validation of 1/12° global ocean fcst. Syst.: Analysis – observation (in-situ) JFM 2013JAS 2013 Largest biases and errors are located near the river mouths, in the western and Eastern Pacific along the Equator, and where sub-meso-scale is significant. MEAN RMS

Ocean Salinity Science - Exeter, UK November Zonal mean anomaly of SMOS and Aquarius: period October 2011 to April 2012 SMOS vs Aquarius data

Ocean Salinity Science - Exeter, UK November SMOS vs Aquarius data Available SSS L3 data in August 2012 : –L3 SMOS data : V01 (CATDS Brest-Ifremer) –L3 Aquarius V1.3 CAP (JPL) SMOS dataAquarius data Level 31/2° - 10 days map1° - 7 days map RFIyes+yes Latitudinal biasyes Ascending/descending phases yes Error at high latitudesyes Wind (retrieval)/surface roughness ECMWFScatterometer SSS (retrieval)ClimatologyHYCOM

Ocean Salinity Science - Exeter, UK November SSS errors in operational oceanography Analysis – observation (in-situ) JFM 2012JAS 2012 Largest biases and errors are located near the river mouths, in the western and Eastern Pacific along the Equator, in the ACC and where sub-meso-scale is significant. MEAN RMS

Ocean Salinity Science - Exeter, UK November Results: impact on in-situ (global) Error: RMS difference between obs. and model forecast Without DA of SSSWith DA of SSS Salinity profiles Temperature profiles No important changes

Ocean Salinity Science - Exeter, UK November SSS errors in operational oceanography Forecast error: RMS(Forecast-Hindcast) JFM 2012JAS 2012 Values do not exceed 0.2 PSU excepted in western boundary currents, ACC, Zapiola eddy where errors can reach 0.5 PSU and even more in region of high runoff (Gulf of Guinea, Bay of Bengal, Amazon and Sea Ice limit) or precipitations (ITCZ, SPCZ).

Ocean Salinity Science - Exeter, UK November Results Without DA of SSSWith DA of SSS innovation incrément

Ocean Salinity Science - Exeter, UK November With Without 16 cm Cloud cover fraction on 20 Nov : the day where SST is assimilated Impact on SLA Obs-fcst in the G. Stream region Without DA of SSSWith DA of SSS

Ocean Salinity Science - Exeter, UK November ARGO vs Aquarius (V1.3) in the ocean forecasting system ARGO – PSY3 (14 Sept. 2011)ARGO – Aquarius (14 Sept. 2011) Global ocean forecasting system has very little bias, it is too salty in the Eastern Pacific & in the Atlantic Aquarius is clearly biased with a predominant zonal pattern (too fresh in the tropics)

Ocean Salinity Science - Exeter, UK November Impact on SLA : global scale 6.9 cm